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Module-Level AI Literacy Integration

Attribution

Original work: "Educators' guide to multimodal learning and Generative AI" — Tünde Varga-Atkins, Samuel Saunders, et al. (2024/25) — CC BY-NC 4.0
Adapted for UK Nursing Education by: Lincoln Gombedza, RN (LD)
Last Updated: December 2025

Integrating AI literacy into individual modules ensures students develop competencies progressively and contextually. This page provides practical guidance for module leaders.

📐 Module Design Principles​

Context is King

Avoid generic "AI Training." Always embed AI literacy within the clinical context of your module (e.g., using AI for care planning in a nursing process module, or for communication simulation in a therapeutic practice module).

1. Alignment with Learning Outcomes​

  • Explicit: Include AI competencies directly in learning outcomes.
  • Mapped: Ensure alignment with NMC standards (e.g., digital literacy, evidence-based practice).

2. Contextual Integration​

  • ✅ DO: Relate AI to specific clinical tasks (care plans, discharge summaries).
  • ❌ DON'T: Teach technology for technology's sake.

🔄 Module Planning Framework​

Follow this 3-step cycle to integrate AI effectively:

📚 Example Module Plans​

Explore how AI integration looks across different fields of nursing:

🫁 Care Planning Module (Year 2)​

Focus: Holistic care planning & Evidence-based practice

Learning Outcomes​

  1. Develop evidence-based care plans using tools including AI.
  2. Critically evaluate AI-generated recommendations against NICE guidelines.
  3. Demonstrate ethical AI use (privacy/accountability).

Key Activities​

  • Week 3 (Demo): Facilitator demonstrates generating a care plan and highlighting errors.
  • Week 4 (Workshop): Students generate plans for complex case studies and red-pen the hallucinations.
  • Week 5 (Ethics): Discussion on data privacy and professional accountability.

Assessment: AI-Enhanced Portfolio​

  • Task: Submit an AI-generated draft + a final human-edited version.
  • Requirement: A 500-word reflection on why changes were made.
  • Success Criteria: Accurate error identification and evidence-based modifications.

⚠️ Common Challenges & Solutions​

Anticipate these hurdles when introducing AI:

Challenge💡 Potential Solution
Student Over-RelianceDesign "AI-Free" components (e.g., oral defense) and require process documentation.
Unequal AccessEnsure all students have access to the same tools (institutional license) or use free tiers with clear guidance.
Academic MisconductMove from "product-based" assessment (the essay) to "process-based" (the portfolio/reflection).
Staff ConfidenceStart small! Introduce AI in just one workshop before a full module rollout.

✅ Implementation Checklist​

For Module Leaders​

  • Review Policy: Check your institution's current AI assessment policy.
  • Tool Check: Ensure the chosen AI tool is GDPR compliant and accessible.
  • Update Handbook: Clearly state "AI Permitted" or "AI Prohibited" for each assessment.
  • Scaffold: Don't assume students know how to prompt; teach them.
  • Safety Net: Have a backup plan if the AI tool goes down during a session.

Assessment Assessment Taxonomy​

  1. 🤖 AI-Enhanced: Students must use AI (e.g., "Critique this AI care plan").
  2. 🤝 AI-Assisted: Students may use AI for specific tasks (e.g., "Brainstorming ideas").
  3. 🚫 AI-Free: No AI permitted (e.g., Clinical exams, Oral defense).

Next: Explore Programme Strategy for curriculum-wide integration.